O. E. Kalashev (Avtor), R. U. Abbasi (Avtor), T. Abu-Zayyad (Avtor), M. Allen (Avtor), Yasuhiko Arai (Avtor), R. Arimura (Avtor), E. Barcikowski (Avtor), J. W. Belz (Avtor), D. R. Bergman (Avtor), J. P. Lundquist (Avtor)

Povzetek

A novel ultra-high-energy cosmic rays energy and arrival direction reconstruction method for Telescope Array surface detector is presented. The analysis is based on a deep convolutional neural network using detector signal time series as the input and the network is trained on a large Monte-Carlo dataset. This method is compared in terms of statistical and systematic energy and arrival direction determination errors with the standard Telescope Array surface detector event reconstruction procedure.

Ključne besede

Telescope Array;indirect detection;surface detection;machine learning;neural network;ground array;ultra-high energy;cosmic rays;energy;arrival directions;reconstruction;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.08 - Objavljeni znanstveni prispevek na konferenci
Organizacija: UNG - Univerza v Novi Gorici
UDK: 539.1
COBISS: 167027459 Povezava se bo odprla v novem oknu
ISSN: 1824-8039
Št. ogledov: 17
Št. prenosov: 0
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

Vrsta dela (COBISS): Delo ni kategorizirano
Strani: str. 1-10
Čas izdaje: 2022
DOI: 10.22323/1.395.0252
ID: 20033968